Search Results for author: Ming Jin

Found 32 papers, 11 papers with code

Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs

no code implementations21 May 2023 Guangsi Shi, Daokun Zhang, Ming Jin, Shirui Pan

To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework.

How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?

no code implementations11 May 2023 Ming Jin, Guangsi Shi, Yuan-Fang Li, Qingsong Wen, Bo Xiong, Tian Zhou, Shirui Pan

In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs.

Time Series Forecasting

LAVA: Data Valuation without Pre-Specified Learning Algorithms

1 code implementation28 Apr 2023 Hoang Anh Just, Feiyang Kang, Jiachen T. Wang, Yi Zeng, Myeongseob Ko, Ming Jin, Ruoxi Jia

(1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between the training and the validation set.

Data Valuation

Geometric Relational Embeddings: A Survey

no code implementations24 Apr 2023 Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He, Michael Cochez, Shirui Pan, Steffen Staab

Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.

Hierarchical Multi-label Classification Knowledge Graph Completion +1

Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance

no code implementations4 Dec 2022 Vanshaj Khattar, Ming Jin

Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high power demand peaks, grid instability exacerbated by intermittent renewable generation, and global climate change amplified by rising carbon emissions.

Decision Making Reinforcement Learning (RL)

On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds

no code implementations2 Dec 2022 Ming Jin, Vanshaj Khattar, Harshal Kaushik, Bilgehan Sel, Ruoxi Jia

We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension.

Non-stationary Risk-sensitive Reinforcement Learning: Near-optimal Dynamic Regret, Adaptive Detection, and Separation Design

no code implementations19 Nov 2022 Yuhao Ding, Ming Jin, Javad Lavaei

We study risk-sensitive reinforcement learning (RL) based on an entropic risk measure in episodic non-stationary Markov decision processes (MDPs).

Reinforcement Learning (RL)

Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning

no code implementations9 Nov 2022 Dawei Gao, Qinghua Guo, Ming Jin, Guisheng Liao, Yonina C. Eldar

Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance.

Recognizing Nested Entities from Flat Supervision: A New NER Subtask, Feasibility and Challenges

no code implementations1 Nov 2022 Enwei Zhu, Yiyang Liu, Ming Jin, Jinpeng Li

However, existing nested NER models heavily rely on training data annotated with nested entities, while labeling such data is costly.

named-entity-recognition Named Entity Recognition +1

Variational Bayesian Inference Clustering Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC

no code implementations25 Oct 2022 Zhaoji Zhang, Qinghua Guo, Ying Li, Ming Jin, Chongwen Huang

Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference based clustering (VBIC) algorithm is developed to solve this clustering problem.

Bayesian Inference Scheduling

Learning Neural Networks under Input-Output Specifications

no code implementations23 Feb 2022 Zain ul Abdeen, He Yin, Vassilis Kekatos, Ming Jin

In this paper, we examine an important problem of learning neural networks that certifiably meet certain specifications on input-output behaviors.

Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

1 code implementation17 Feb 2022 Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan

Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.

Multivariate Time Series Forecasting Traffic Prediction

From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach

no code implementations11 Feb 2022 Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen

Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.

Anomaly Detection Contrastive Learning

Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming

no code implementations20 Nov 2021 Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, Zhao Li

To overcome the aforementioned problems, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.

Contrastive Learning Graph Representation Learning +1

Adversarial Unlearning of Backdoors via Implicit Hypergradient

3 code implementations ICLR 2022 Yi Zeng, Si Chen, Won Park, Z. Morley Mao, Ming Jin, Ruoxi Jia

Particularly, its performance is more robust to the variation on triggers, attack settings, poison ratio, and clean data size.

Towards General Robustness to Bad Training Data

no code implementations29 Sep 2021 Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia

In this paper, we focus on the problem of identifying bad training data when the underlying cause is unknown in advance.

Data Summarization

Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs

no code implementations29 Sep 2021 Ming Jin, Yuan-Fang Li, Yu Zheng, Bin Yang, Shirui Pan

Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data.

Graph structure learning Representation Learning +1

Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems

1 code implementation8 Sep 2021 Fangda Gu, He Yin, Laurent El Ghaoui, Murat Arcak, Peter Seiler, Ming Jin

Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity.


Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

1 code implementation23 Aug 2021 Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Yi-Ping Phoebe Chen

While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.

Anomaly Detection Contrastive Learning +2

MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition

1 code implementation16 Jul 2021 Hao Chen, Ming Jin, Zhunan Li, Cunhang Fan, Jinpeng Li, Huiguang He

Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation.

Domain Adaptation EEG Emotion Recognition +2

A Unified Framework for Task-Driven Data Quality Management

no code implementations10 Jun 2021 Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia

High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM).

Data Summarization Data Valuation +1

Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning

1 code implementation12 May 2021 Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan

To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.

Contrastive Learning Graph Representation Learning

Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach

no code implementations2 May 2021 Sarthak Gupta, Vassilis Kekatos, Ming Jin

The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions.

Unitary Approximate Message Passing for Sparse Bayesian Learning

no code implementations25 Jan 2021 Man Luo, Qinghua Guo, Ming Jin, Yonina C. Eldar, Defeng, Huang, Xiangming Meng

Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm.

Variational Inference

Imitation Learning with Stability and Safety Guarantees

1 code implementation16 Dec 2020 He Yin, Peter Seiler, Ming Jin, Murat Arcak

A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL).

Imitation Learning

Power up! Robust Graph Convolutional Network based on Graph Powering

no code implementations25 Sep 2019 Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi

By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.

Adversarial Robustness

Power up! Robust Graph Convolutional Network via Graph Powering

1 code implementation24 May 2019 Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi

By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.

Adversarial Robustness

Stability-certified reinforcement learning: A control-theoretic perspective

no code implementations26 Oct 2018 Ming Jin, Javad Lavaei

We investigate the important problem of certifying stability of reinforcement learning policies when interconnected with nonlinear dynamical systems.

reinforcement-learning Reinforcement Learning (RL)

Inverse Reinforcement Learning via Deep Gaussian Process

no code implementations26 Dec 2015 Ming Jin, Andreas Damianou, Pieter Abbeel, Costas Spanos

We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations.

reinforcement-learning Reinforcement Learning (RL)

Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation

no code implementations22 Jun 2014 Ming Jin, Han Zou, Kevin Weekly, Ruoxi Jia, Alexandre M. Bayen, Costas J. Spanos

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors.

energy management Management

Cannot find the paper you are looking for? You can Submit a new open access paper.